Constructing spatial neighborhood with custom parameters

preprocessing/neighbors/delaunay_traingulation/config/config_1.json

@@ -0,0 +1,3 @@
+{
+    "delaunay":false
+}

preprocessing/neighbors/delaunay_triangulation.py → preprocessing/neighbors/delaunay_traingulation/delaunay_triangulation.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 
 # Author_and_contribution: Niklas Mueller-Boetticher; created script
+# Author_and_contribution: Qirong Mao; modifying output file format
 
 import argparse
 
@@ -21,7 +22,7 @@
 parser.add_argument(
     "-o", "--observations", help="Path to observations (as tsv).", required=True
 )
-parser.add_argument("-d", "--out_file", help="Output file.", required=True)
+parser.add_argument("-d", "--out_dir", help="Output directory.", required=True)
 parser.add_argument(
     "--config",
     help="Optional config file (json) used to pass additional parameters.",
@@ -33,18 +34,29 @@
 # Output files
 from pathlib import Path
 
-neighbor_file = Path(args.out_file)
-# if additional output files are required write it also to out_dir
+out_dir = Path(args.out_dir)
+
+spatial_connectivities_file = out_dir / "spatial_connectivities.mtx"
+##spatial_distances_file = out_dir / "spatial_distances.mtx"
+
 
 # Use these filepaths and inputs ...
 coord_file = args.coordinates
 matrix_file = args.matrix
 feature_file = args.features
 observation_file = args.observations
 
+## Loading delaunay parameters from config_file
 if args.config is not None:
     config_file = args.config
 
+import json
+
+with open(config_file) as f:
+   parameters = json.load(f)
+
+delaunay = parameters["delaunay"]
+
 
 # ... or AnnData if you want
 def get_anndata(args):
@@ -76,12 +88,15 @@ def get_anndata(args):
 ## Your code goes here
 import squidpy as sq
 
-sq.gr.spatial_neighbors(adata, delaunay=True, coord_type="generic")
-neighbors = adata.obsp["spatial_connectivities"].astype(int)
+sq.gr.spatial_neighbors(adata, delaunay=delaunay, coord_type="generic")
 
+neighbors = adata.obsp["spatial_connectivities"].astype(int)
+##distance = adata.obsp["spatial_distances"].astype(float)
 
 ## Write output
 import scipy as sp
 
-neighbor_file.parent.mkdir(parents=True, exist_ok=True)
-sp.io.mmwrite(neighbor_file, neighbors)
+out_dir.mkdir(parents=True, exist_ok=True)
+
+sp.io.mmwrite(spatial_connectivities_file, neighbors)
+##sp.io.mmwrite(spatial_distances_file, distance)            

preprocessing/neighbors/n_neighbourhood/config/config_1.json

@@ -0,0 +1,3 @@
+{
+    "n_neighs":6
+}

preprocessing/neighbors/n_neighbourhood/n_neighbourhood.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+
+# Author_and_contribution: Niklas Mueller-Boetticher; created script, 
+# Author_and_contribution: Qirong Mao; implemented method
+
+import argparse
+
+# TODO adjust description
+parser = argparse.ArgumentParser(
+    description="Neighbor definition based on numbers of neibourhood (only for generic coordinates)"
+)
+
+parser.add_argument(
+    "-c", "--coordinates", help="Path to coordinates (as tsv).", required=True
+)
+
+parser.add_argument(
+    "-m", "--matrix", help="Path to (transformed) counts (as mtx).", required=True
+)
+
+parser.add_argument(
+    "-f", "--features", help="Path to features (as tsv).", required=True
+)
+
+parser.add_argument(
+    "-o", "--observations", help="Path to observations (as tsv).", required=True
+)
+
+parser.add_argument("-d", "--out_dir", help="Output directory.", required=True)
+
+parser.add_argument(
+    "--config",
+    help="Optional config file (json) used to pass additional parameters.",
+    required=False,
+)
+
+args = parser.parse_args()
+
+# Output files
+from pathlib import Path
+
+out_dir = Path(args.out_dir)
+
+spatial_connectivities_file = out_dir / "spatial_connectivities.mtx"
+##spatial_distances_file = out_dir / "spatial_distances.mtx"
+
+# Use these filepaths and inputs ...
+coord_file = args.coordinates
+matrix_file = args.matrix
+feature_file = args.features
+observation_file = args.observations
+
+## Loading n_neighs parameter from config_file
+
+if args.config is not None:
+    config_file = args.config
+
+import json
+
+with open(config) as f:
+   parameters = json.load(f)
+
+n_neighs = data["n_neighs"]
+
+# ... or AnnData if you want
+def get_anndata(args):
+    # Untested template
+    import anndata as ad
+    import pandas as pd
+    import scipy as sp
+
+    X = sp.io.mmread(args.matrix)
+    if sp.sparse.issparse(X):
+        X = X.tocsr()
+    observations = pd.read_table(args.observations, index_col=0)
+    features = pd.read_table(args.features, index_col=0)
+    coordinates = (
+        pd.read_table(args.coordinates, index_col=0)
+        .loc[observations.index, :]
+        .to_numpy()
+    )
+
+    adata = ad.AnnData(
+        X=X, obs=observations, var=features, obsm={"spatial": coordinates}
+    )
+
+    return adata
+
+
+adata = get_anndata(args)
+
+## Your code goes here
+import squidpy as sq
+
+sq.gr.spatial_neighbors(adata, n_neighs=n_neighs, coord_type="generic")
+
+neighbors = adata.obsp["spatial_connectivities"].astype(int)
+##distance = adata.obsp["spatial_distances"].astype(float)
+
+## Write output
+import scipy as sp
+
+out_dir.mkdir(parents=True, exist_ok=True)
+
+sp.io.mmwrite(spatial_connectivities_file, neighbors)
+##sp.io.mmwrite(spatial_distances_file, distance)              

preprocessing/neighbors/n_neighbourhood/n_neighbourhood.yml

@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+dependencies:
+  - python=3.9.18
+  - pip
+  - pip:
+    - squidpy==1.3.1

preprocessing/neighbors/n_rings/config/config_1.json

@@ -0,0 +1,3 @@
+{
+    "n_rings":1
+}

preprocessing/neighbors/n_rings/n_rings.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+
+# Author_and_contribution: Niklas Mueller-Boetticher; created script, 
+# Author_and_contribution: Qirong Mao; implemented method
+
+
+import argparse
+
+# TODO adjust description
+parser = argparse.ArgumentParser(
+    description="Neighbor definition based on number of rings of neighbors (only for grid coordinates)"
+)
+
+parser.add_argument(
+    "-c", "--coordinates", help="Path to coordinates (as tsv).", required=True
+)
+
+parser.add_argument(
+    "-m", "--matrix", help="Path to (transformed) counts (as mtx).", required=True
+)
+
+parser.add_argument(
+    "-f", "--features", help="Path to features (as tsv).", required=True
+)
+
+parser.add_argument(
+    "-o", "--observations", help="Path to observations (as tsv).", required=True
+)
+
+parser.add_argument("-d", "--out_dir", help="Output directory.", required=True)
+
+parser.add_argument(
+    "--config",
+    help="Optional config file (json) used to pass additional parameters.",
+    required=False,
+)
+
+args = parser.parse_args()
+
+# Output files
+from pathlib import Path
+
+out_dir = Path(args.out_dir)
+
+spatial_connectivities_file = out_dir / "spatial_connectivities.mtx"
+##spatial_distances_file = out_dir / "spatial_distances.mtx"
+
+# Use these filepaths and inputs ...
+coord_file = args.coordinates
+matrix_file = args.matrix
+feature_file = args.features
+observation_file = args.observations
+
+
+## Loading delaunay parameters from config_file
+if args.config is not None:
+    config_file = args.config
+
+import json
+
+with open(config_file) as f:
+   parameters = json.load(f)
+
+n_rings = parameters["n_rings"]
+
+
+# ... or AnnData if you want
+def get_anndata(args):
+    # Untested template
+    import anndata as ad
+    import pandas as pd
+    import scipy as sp
+
+    X = sp.io.mmread(args.matrix)
+    if sp.sparse.issparse(X):
+        X = X.tocsr()
+    observations = pd.read_table(args.observations, index_col=0)
+    features = pd.read_table(args.features, index_col=0)
+    coordinates = (
+        pd.read_table(args.coordinates, index_col=0)
+        .loc[observations.index, :]
+        .to_numpy()
+    )
+
+    adata = ad.AnnData(
+        X=X, obs=observations, var=features, obsm={"spatial": coordinates}
+    )
+
+    return adata
+
+
+adata = get_anndata(args)
+
+## Your code goes here
+import squidpy as sq
+
+sq.gr.spatial_neighbors(adata,n_rings=n_rings, coord_type="grid")
+
+neighbors = adata.obsp["spatial_connectivities"].astype(int)
+##distance = adata.obsp["spatial_distances"].astype(float)
+
+## Write output
+import scipy as sp
+
+out_dir.mkdir(parents=True, exist_ok=True)
+
+sp.io.mmwrite(spatial_connectivities_file, neighbors)
+##sp.io.mmwrite(spatial_distances_file, distance)             

preprocessing/neighbors/n_rings/n_rings.yml

@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+dependencies:
+  - python=3.9.18
+  - pip
+  - pip:
+    - squidpy==1.3.1

preprocessing/neighbors/radius/config/config_1.json

@@ -0,0 +1,3 @@
+{
+    "radius":1
+}